%0 Journal Article
%T Real-Time Behaviour Planning and Highway Situation Analysis Concept with Scenario Classification and Risk Estimation for Autonomous Vehicles
%J Designs | An Open Access Journal from MDPI
%D 2019
%R https://doi.org/10.3390/designs3010004
%X The development of autonomous vehicles is one of the most active research areas in the automotive industry. The objective of this study is to present a concept for analysing a vehicle¡¯s current situation and a decision-making algorithm which determines an optimal and safe series of manoeuvres to be executed. Our work focuses on a machine learning-based approach by using neural networks for risk estimation, comparing different classification algorithms for traffic density estimation and using probabilistic and decision networks for behaviour planning. A situation analysis is carried out by a traffic density classifier module and a risk estimation algorithm, which predicts risks in a discrete manoeuvre space. For real-time operation, we applied a neural network approach, which approximates the results of the algorithm we used as a ground truth, and a labelling solution for the network¡¯s training data. For the classification of the current traffic density, we used a support vector machine. The situation analysis provides input for the decision making. For this task, we applied probabilistic networks. View Full-Tex
%U https://www.mdpi.com/2411-9660/3/1/4